A STUDY ON ACCESSIBILITY AND GOING-OUT BEHAVIOR OF AGEDPEOPLE CONSIDERING DAILY ACTIVITY PATTERN
Nobuaki OHMORI
Department of Urban Engineering
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo,113-8656 Japan
Fax: +81-3-5841-8527
E-mail:nobuaki@ut.t.u-tokyo.ac.jp

Yasunori MUROMACHI
Engineering Research Institute
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo,113-8656 Japan
Fax: +81-3-5841-8527
E-mail:ymuro@ut.t.u-tokyo.ac.jp

Noboru HARATA
Graduate School of Frontier Sciences
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo,113-8656 Japan
Fax: +81-3-5841-8527
E-mail:nhara@ut.t.u-tokyo.ac.jp

Katsutoshi OHTA
Department of Urban Engineering
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo,113-8656 Japan
Fax: +81-3-5841-8527
E-mail:katsuohta@ut.t.u-tokyo.ac.jp

Abstract: This paper examines the relationships between accessibility and going-out
behavior of aged people in a framework of space-time constraints. We collected activity
diary data together with data of desired patterns of time use and satisfaction level in daily
life. The analysis shows that some aged people desire to do more in-home discretionary
activities and to go out more frequently to do out-of-home discretionary activities.
Non-working aged people who satisfied more with their daily life go out more often to do
discretionary activities and the amount of time spent on out-of-home activities is larger. By
developing a choice model, it is revealed that accessibility to activity opportunities
available in the space-time prism effect the choice between in-home and out-of-homediscretionary activity.

1.INTRODUCTION
For the aged society it is important in urban planning to provide user-friendly transport
environment so that aged people can live their independent life in community. Recently,
motorization has been progressing especially in local cities and the trends of large-scale
facilities (shopping center, hospital, etc.) moving to the suburbs and causing inner city
problem. Since the service level of public transport has been lower, the important issue isthat the gap in mobility and accessibility becomes larger between people who can use car
and not.
Since travel is a derived demand for engaging in activities located at different places, it is avery useful method to analyze activities in order to understand travel behavior. A
fundamental descriptor of individuals’ activity engagement is time use - how much time isallocated to each type of activity over a span of time, and when each episode of activity
starts and how long it lasts. “NHK national daily time use survey (NHK 1996)”, a
representative time use survey in Japan, reports the following remarkable differencescomparing time use for people over and under 60 years old. For people over 60;-The amount of time spent on mandatory activity increase, for example, sleep, personalcare and medical.
-The amount of time spent on paid work and domestic work decrease.
-The amount of time spent on discretionary activity increase.
-The amount of time spent on out-of-home activity decrease.
-The variability between days of week becomes small.
These changes and characteristics would also effect the going-out behavior of aged people.
It is necessary to reveal a demand of latent discretionary out-of-home activities and torelax travel resistance to do mandatory out-of-home activities for aged people. To reveal
the relationships between daily activity including in-home activities and going-out
behavior, an activity-based approach considering space-time constraints using activity
diary and opportunity data should be most useful. There seems to be few previous studies
analyzing travel behavior of aged people using activity diary data. This paper aims toanalyze the relationships between accessibility and going-out behavior considering
constraints of daily activity patterns of aged people using activity diaries, detailed data on
activity opportunity and transportation network data in a framework of space-time
constraints. First, their desired patterns of daily time use of in-home activities and thefrequency of out-of-home activities and the relationships between level of satisfaction withdaily life and out-of-home activities are analyzed. Next, accessibility to activity
opportunities is measured in space-time constraints and the impact of accessibility on
out-of-home discretionary activities is analyzed by modeling the choice behavior between
out-of-home and in-home activity participation.

2.THE CONCEPT OF ACCESSIBILITY AND SPACE-TIME PRISM
Accessibility affects travel behavior, it has thus been important indices in urban
transportation planning. The concept of accessibility is generally interpreted as a measureof the effort (or ease) of overcoming spatial separation. Accessibility measures can beloosely organized into three types: (a) cumulative opportunities measures, (b)gravity-based measures, and (c) utility-based measures (Handy 1997).
(a) Cumulative opportunities measures
These measures of accessibility count the number of opportunities reached within a given
travel time (or distance).
(b) gravity-based measures
This type of measure weights opportunities, usually the quantity of an activity as measured

by employment, by impedance, generally a function of travel time or travel cost.
For example:
Ai = ∑ O j f (Cij )(1)jwhere:
Ai = accessibility from zone i to the relevant type of opportunities
Oj = opportunities of that type present in zone j (employment places, shops etc.)
Cij= generalized (or actual) time or cost for a trip from i to jf(Cij) = impedance function – an exponential or power function is generally used.
(c) utility-based measures
This measure is based on random utility theory and present expected maximum utility ofchoice alternatives.
An = ln( ∑ exp(Vn ( c ) ))

(2)

∀∈Cn

where:Vn (c ) = the utility of choice c for person nCn =the choice set for person n

We describe our concept of accessibility and going-out behavior within constraints asembodied by space-time prism (Hägerstrand 1970, Lenntorp 1976, Miller 1991, Wang, etal. 1996). A person lives in a location in a city where many opportunities are located atvarious distances from his home.
We classify activities that he does on that day into three types (Figure 1(a)), which are:Activity (a) – the activities for which time of day, duration and location are fixed.
Activity (b) – the activities for which duration and location are fixed.
Activity (c) – the activities that do not have to be performed and for which the time ofday, duration and location are not fixed.
He must do activities(a) at a fixed time of day, for a certain duration, at a certain location.
But he can change schedule of activities(b) and he can do the activities at different time ofday, between the stop time for an activitiy(a) and the start time for the next activity(a).
Then he has the amounts of time which he can allocate to any kind of activities(c) or travel.
If he changes schedule of activities(b) as shown in Figure 1(b) so that available time can be
combined into longer time period to which activities(c) or travel can be allocated (“freetime” in Figure 1(b)). The space-time prism or the potential path space (PPS) is determined.
Since each opportunity has its opening hours, he can have the amount of maximum
available time spent at the opportunity by changing schedule of activities(b).

＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊

time
Activity (a)Activity (b)Activity (c)“free time”

Figure 1(a). Classify Activities
in that Day

The range to which
the prism can be
adjusted

The amount of
maximum available
time spent at an
opportunity
The stop time for an
activity (a)Figure 1(b). Changing Schedule of Activities(b)and Calculating the Amount of Maximum
Available Time Spent at an Opportunity＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊ ＊＊＊＊＊＊＊＊＊＊＊＊＊

Activity(a)Activity(b)Feasible space-time path if
slower mode is available＊ available opportunity using＊

faster mode

not available opportunity

The difference of PPS(depends on available mode)

＊＊＊＊

＊

home

＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊

●
＊＊＊＊

＊＊＊＊

＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊ ＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊＊ ＊＊＊＊＊＊＊

time

The start time forthe next activity (a)The difference of PPA(depends on available mode)

space

Figure 1(c). Out-of-home and In-home Activity Participation in Space-time Prism

The PPS depends on velocity of his available mode, for example, the PPS is large when hecan use a car, but the PPS is smaller when he can’t use a car and he must use slower mode
(Figure 1(c)). Projecting the PPS onto the planar space provides the potential path area
(PPA). The PPA presents his potential area reached at specified times (in a realenvironment, the PPA is not the shape of a circle but the very complicated shape dependent
on the transportation network). Not all opportunities within the PPA are available for him
since each opportunity has its opening hours and therefore he can only use activity
opportunities of which the opening hours is contained in the PPS. In this study, we define
accessibility to an activity opportunity as the travel impedance multiplied by the maximum
available time spent at the activity opportunity in the PPS (we call this accessibility“space-time accessibility”). Accessibility Aijp of individual i to opportunity j in prism p isdefined the following:
Aijp = X jp a j f (Cijp )T jp(3)where:
Xjp=

1 if opportunity j is available in prism p (free time minus travel time to and from
opportunity j > 0 and opening hours is contained in prism p); 0 otherwise
aj=
attractiveness of opportunity j
Cijp = travel cost from individual i’s home to opportunity j in prism pf(Cijp ) = impedance function (exponential function)Tjp =the amount of maximum available time spent at opportunity j in prism p

When he faces a prism at a certain time of day, he can choose an in-home or out-of-homediscretionary activity participation comparing the utilities of each alternative. Accessibility
is considered to be one of the elements of the utility of out-of-home discretionary activity.
Lenntorp (1976, 1978) presented a so-called “Program Evaluating the Set of Alternative
Sample Paths (PESASP)” model. The program computes the number of feasible paths
between two stations (e.g. home and workplace), given activity programs of individuals
and the physical environmental constraints (e.g. the transport system and the locations andopening hours of other stations). Some recent studies evaluated improvement plansconsidering feasibility of activity programs based on the PESASP model, for example, theactivity of a working mother taking child to nursery, the activity of aged people going tohospital (Segawa and Sadahiro 1996, Ohmori, et al. 1998, etc.). In this study, we consider
the effect of feasibility of doing activity at an opportunity on travel behavior, taking into
account the amount of available time spent at the opportunity and the time of day.
To do this kind of analysis, we have to prepare the following kinds of data:-Transport demand – activity diary (time use data), individual and household
characteristics and home location-Transport supply –road and public transportation network (bus stops, routes andtime table)-Activity opportunity – location and opening hours

We did a survey collecting activity diary data of aged people for transport demand data.
For transport supply and activity opportunity data, we used the existent road network data,but made by ourselves bus network data, activity opportunity locations and opening hours
data using GIS (MapInfo).

3. ACTIVITY DIARY SURVEY AND DESIRED PATTERN FOR DAILY TIMEUSE OF AGED PEOPLE
We did a survey collecting activity diaries and information concerning daily going-out
behavior and consciousness about daily life in Akita City (about 300,000 Population,
14.5% over 65 years old, 1995 Census data), where the ratio of aged people is likely to bevery high in near future (Ohmori, et al. 1997). This survey was conducted by drop off andmail back method. 190 samples were collected for people over 60 years old, 144 of them
have activity diary data on a weekday. All respondents were asked to record not only
out-of-home activities and related travel but also in-home activities. Table 1 shows the
sample’s basic indices about travel behavior. The common trend is seen from this surveythat older people go out less.
Table 1. Basic Indices about Travel Behavior
60 – 64 years 65 – 74 years
0.90
0.70
Frequency of going-out (times／day)
Ratio of going-out
0.82
0.72
2.32
2.02
Trip rate (trips／day)
1.05
1.00
Tour rate (tours／day)Time spent on out-of home (hours)
5.31
3.82

75 years 0.51
0.55
1.55
0.79
1.63

In this survey, we questioned respondents about their desired pattern of time use forin-home activities and frequency of out-of-home activities in daily life. Figures 2 and 3
show whether they want to increase or decrease the amount of time spent on each in-homeactivity. Most people answered “the present amount of time is good enough” for eachactivity, but some people desired to decrease the amount of time spent on paid work anddomestic work (preparing meal, washing and cleaning), and to increase discretionary
activities (hobbies and entertainment, social, resting, etc.). Since working people would beconstrained strongly by mandatory activities, the difference between the present anddesired time use pattern is larger among working people than among non-working people.
This result suggests that some aged people are not satisfied with their present time use
pattern and some constraints prevent the desired time use pattern.

resting
more than 60
minutes increase
30 - 60 minutes
increase0 - 30 minutes
increase
the present amountis good enough0 - 30 minutes
decrease
30 - 60 minutes
decrease
more than 60
minutes decrease

reading
TV watchinghobbies and entertainmentsocialgrandchild carewashing and cleaning
preparing meal
personal care
meal
sleep
0%

20%

40%

60%

80%

100%

Figure 2. Desire for the Amount of Time Spent on In-home Activities of Non-working
Peoplerestingreading

more than 60
minutes increase
30 - 60 minutes
increase0 - 30 minutes
increase
the present amountis good enough0 - 30 minutes
decrease
30 - 60 minutes
decrease
more than 60
minutes decrease

TV watchinghobbies and entertainmentsocialpaid work
grandchild carewashing and cleaning
preparing meal
personal care
meal
sleep
0%

20%

40%

60%

80%

100%

Figure 3. Desire for the Amount of Time Spent on In-home Activities of Working People
Figures 4 and 5 show desire for the frequency of out-of-home activities. The trend isobserved that some people desire to decrease the frequency of paid work and medical, anddesire to increase the frequency of entertainment, traveling and visiting son’s house. The
difference between their present and desired condition is also larger among working people
than among non-working people.
more than 2 times/week increase

medicalson's house

1 time/week increase

traveling

1 time/month increase
the present frequency is goodenough1 time/month decrease

entertainment
errands

1 time/week decrease

shopping
0%

20%

40%

60%

80%

100%

more than 2 times/week decrease

Figure 4. Desire for Frequency of Out-of-home Activities of Non-working Peoplemore than 2 times/week increase

paid work
medical1 time/week increase

son's house

1 time/month increase
the present frequency is goodenough1 time/month decrease

travelingentertainment

1 time/week decreaseerrands

more than 2 times/week decrease

shopping
0%

20%

40%

60%

80%

100%

Figure 5. Desire for Frequency of Out-of-home Activities of Working People
Figure 6 shows level of satisfaction in daily life and the frequency of out-of-homediscretionary activities for non-working people. Figure 7 shows level of satisfaction in thesurvey day and the amount of time spent on out-of-home discretionary activities in the day.
People showing higher level of satisfaction with their daily life have higher frequency ofout-of-home discretionary activities, and people showing higher level of satisfaction in thesurvey day spend longer time in out-of-home discretionary activities. Among working
people, such a trend is not observed.
The results of this chapter suggest that aged people desire more out-of-home discretionary
activities and less out-of-home mandatory activities. Out-of-home discretionary activity
would affect their level of satisfaction with daily life. Therefore it would be important toprovide an environment in which aged people can easily go out to do out-of-homediscretionary activities and to relax travel resistance for mandatory out-of-home activities.
Since some people desire to do more social activity in their house as shown in figure 2, it isalso important to provide an environment in which their friends or other people can easily
come to their house. In this sense, accessibility to activity opportunities would be a very
important factor in urban transportation planning for the aged society.

Satisfied0 - 0.5times/day

Fairly satisfied

0.5 - 1times/day
Neither satisfied nor dissatisfied.

1 - 1.5times/day
1.5times/day -A little dissatisfied
Dissatisfied
0%20%

40% 60%80% 100%

Figure 6. Level of Satisfaction in Daily Life and the Frequency of Out-of-home
Discretionary Activities of Non-working People

Satisfied

0
0 - 1hour

Fairly satisfied

1 - 2hours
2 - 3hours

Neither satisfied nor dissatisfied.

3hours 0%20%

40% 60%80% 100%

Figure 7. Level of Satisfaction in the Day and the Amount of Time Spent at Out-of-home
Discretionary Activities of Non-working People
Comparing aged people with other people, generally aged people have not only stronger
physical constraints but also constraints of daily mandatory activities (sleeping, having
meals, etc.) to keep up their health and their custom. In this survey, we questioned about
their regularity of location and time of day for wake-up, go-to-bed and having meals.
About 80 to 90% of the respondents reported “Everyday I wake up, go to bed and havemeals at fixed time”. As concerns location for having meals, non-working people reported“Everyday I have meals at my house”, for breakfast 97%, lunch 88% and dinner 98% of
the respondents respectively. Based on these results, we classified their activities into three
types according to the classification described in chapter 2:
Activity (a) – sleeping, having meals, paid work and medical
Activity (b) – personal care and domestic work
Activity (c) – hobbies and entertainment, reading, TV viewing, etc. except for Activity (a)and Activity (b)Medical activity is classified into activity(a) including related travels, since no respondent
did multi-purpose trips with medical trips in this survey. It is considered based on thisclassification that non-working aged people have three or four prisms in a day (for example,
a prism is constructed between get-up and breakfast, breakfast and lunch, lunch and dinner,
dinner and go-to-bed), all location of activity(a) is their home, so fixed station is always
their home. In the next chapter, we measure accessibility based on these assumptions.

4.MEASURING ACCESSIBILITY USING TRANSPORT DEMAND, TRANSPORTSUPPLY AND ACTIVITY OPPORTUNITY DATA
We prepared location point data (respondents’ home, activity opportunities and bus stops)
and network data using GIS to measure accessibility. In this study, the index ofaccessibility to an activity opportunity is defined as the travel impedance multiplied by theamount of maximum available time in the PPS (shown in chapter 2). First we need to
calculate travel cost from their home to each opportunity. Travel cost by automobile andwalk is assumed travel time using the route of minimum travel time calculated on the road
network. Travel cost by bus is assumed generalized travel time using the route of minimum
generalized time, which represents physical resistance in traveling, using equivalentcoefficients of time spent in various travel modes. Equivalent coefficient means thecoefficient which change the amount of time of several travel modes in a trip to the amountof time of base mode (Nitta, et al. 1995). For example, in Table 2, the coefficient 2.38 ofwalking means that aged people feel 2.38 times the resistance to walking than traveling bysitting on bus. In this study, we used the generalized travel time changed to sitting on bus
mode, postulated that aged people can sit on bus, using these equivalent coefficients. Table
2 also shows that aged people feel more resistance than other people especially in transfer
and walking mode.
Table 2. Equivalent Coefficients (Shin 1997)
Sitting on Bus Walking Transfer
Aged People
1.00
2.38
5.26
Other People
1.00
1.92
3.45

Waiting
1.92
1.82

Bus network includes walking access links, waiting links and transferring links besides onboard links, but the bus fare is not considered in this study. Automobile speed is set at
26km/h in DID (Densely Inhabited District) and at 39km/h not in DID, bus speed is set at
20km/h in DID and at 30km/h not in DID (calculated from the time table of bus in Akita
City, and automobile speed is set to 1.3 times faster than bus speed referring to the existentperson trip survey report). Walking speed is set at 4km/h and the time for waiting andtransferring are calculated using the equation to estimate waiting time from headwayreferring to the person trip survey report.
Travel costs from respondents’ home to activity opportunities by walk, automobile and bus
are calculated using these transportation network data. In this survey the respondents wereasked to report the activity opportunities which they often use in daily life. Based on thisdata, the activity opportunities which they often use are used in this study (department
store, public facilities, supermarket, etc.). We set the location and opening hours of each
opportunity and calculate whether each opportunity is available for a person in a prism
using his available mode. If the opportunity is available for him, next we measure theamount of maximum available time spent in the opportunity. We define the accessibility toan opportunity as the exponential function of travel cost multiplied by the amount of
maximum available time spent at the opportunity. Figure 8 shows the distribution of
respondents’ homes and activity opportunities (the major department store, public facility,
supermarket), road and bus network in Akita City.

respondent's homemajor department store and public facility
supermarket
bus stop0

1

2

Kilometers

Figure 8. Distribution of Respondents’ Home and Activity Opportunities (Department
Store, Public Facilities, Supermarket), Road and Bus Network in Akita City

5.
EFFECT OF ACCESSIBILITY ON THE CHOICE
OUT-OF-HOME AND IN-HOME ACTIVITY PARTICIPATIONBETWEEN

In this chapter, we try to develop choice models to examine the effect of accessibility on
out-of-home activity participation. Based on the concept shown in chapter 2, the choice
between in-home and out-of-home discretionary activity participation is modeled in eachspace-time prism. We developed the model using only samples of non-working people.
The models are binary logit models, and the difference of utility between out-of-home andin-home activity is assumed to be affected by individual and household characteristics (age,gender, household type, etc.), availability of automobile, accessibility to opportunities andtime of day. We define the probability of participating in out-of-home activity in a prism is:
Pout =

exp(Vout )exp(Vout ) + exp(Vin )(4)

Vout = ∑αi X i(5)

Vin = 0(6)iwhere:Vout = utility of out-of-home discretionary activity
Vin = utility of in-home discretionary activity
αi = parameter
Xi = explanatory variable
The explanatory variables of the models are summarized in Table 3.
Table 3. Variables Used in the Analysis
Variable
DescriptionAge
Age of individual (years old)
Gender
1 male; 0 female
Household1 if individual has someone who does domestic work in household; 0
otherwise
Access 1W
Accessibility to the opportunity (1) of maximum accessibility on footAccess 2A
The sum of the accessibility to opportunities (2) by automobile specificto individual who can use a car
Access 2B
The sum of the accessibility to opportunities by bus specific toindividual who can’t use a car
ST Access 1W Space-time accessibility to the opportunity (1) of maximum
accessibility on footST Access 2A The sum of the space-time accessibility to opportunities (2) byautomobile specific to individual who can use a car
ST Access 2B The sum of the space-time accessibility to opportunities by bus specificto individual who can’t use a car
Time GB
1 if prism is between getting up and breakfast; 0 otherwise
Time BL
1 if prism is between breakfast and lunch; 0 otherwise
Time LD
1 if prism is between lunch and dinner; 0 otherwise
Out-of-home
1 alternative specific dummy to out-of-home activity
We classify various opportunities into two types according to its characteristics whenpeople use the opportunity:
Opportunity (1) – people use only the opportunity at the minimum cost to travel
Opportunity (2) – people want to use various opportunities dependent on the situation
For example, people tend to use the nearest opportunity in case of traveling to grocery
store, post office, bank and so on, but it would be better for them to have many alternatives
in case of visiting department store, public facility and so on. According to this hypothesis
and the definition in chapter 2, the following accessibility variables are introduced to themodels:W
Access 1W = max1 [ X jp a j exp(−λCijp)](7)

Access 2A = ∑ X jp a j exp(−λCijpA )(8)

Access 2B = ∑ X jp a j exp(−λCijpB )(9){ j| j∈O j }j| j∈O 2j

j| j∈O 2j

W
ST Access 1W = max1 [ X jp a j exp(−λCijp)T jp ]
{ j| j∈O j }(10)

ST Access 2A = ∑ X jp a j exp(−λCijpA )T jp(11)

ST Access 2B = ∑ X jp a j exp(−λCijpB )T jp(12)j| j∈O 2j

j| j∈O 2j

where:W
Cijp= Travel time by walk

CijpA = Travel time by automobile
CijpB = Generalized travel time by bus
O1j = Opportunity set 1 (supermarket)
O 2j = Opportunity set 2 (department store, public facility, etc.)
ST Access 1W, 2A and 2B are space-time accessibility described in chapter 2, while
Access 1W, 2A and 2B are space accessibility which does not consider the amount ofavailable time in opportunities. It is assumed that aj is constant to all opportunity in thisstudy (aj =1). (ST)Access 1W assumes that individuals only consider the best alternative inthe choice set, while (ST)Access 2A and (ST)Access 2B assume that individuals consider
the entire choice set. To represent the difference of the time of prism in the day, dummy
variables are also introduced setting four time slots, morning, before noon, afternoon andevening, which are divided by sleep and meals activities. Coefficient estimates andt-statistics are summarized in Table 4.

Variable
Age
GenderHousehold
Access 1W
Access 2A
Access 2B
ST Access 1W
ST Access 2A
ST Access 2BTime GBTime BLTime LD
Out-of-home
Rho-Square
Hit Ratio
No. of Observation

Table 4. Model Estimation Results
Model 1Model 2
Coefficientt-value
Coefficientt-value-0.0731-2.931-0.0906-3.337-0.1844-0.423-0.2129-0.494-0.1073-0.263-0.1999-0.477-0.7095-0.821
0.19842.715
0.41462.340
0.03080.478
0.01433.100
0.03222.741
3.0882
3.099
3.11303.174
2.15462.313
2.18702.368
2.61122.784
2.11122.203
2.24860.601
4.7962
1.210
0.391
0.439
78.8%
81.3%
240
240

In both Model 1 and Model 2, negative and significant coefficient Age shows that older
individuals do less out-of-home discretionary activities. Gender and Household are notsignificant but negative, implying that male do less out-of-home discretionary activities
than female, and individuals who have other individuals doing domestic work in his
household do less out-of-home activities (maybe shopping). The positive coefficientestimates of Time GB, BL and LD imply that when aged people face the same accessibility,
they tend to go out in the morning, before noon and afternoon rather than evening. As forAccessibility variables, except for Access 1W and ST Access 1W, these are significant
coefficients, implying that individuals who have higher accessibility tend to doout-of-home discretionary activities. Comparing Model 1 and Model 2, ST Access 2A and2B that represent space-time accessibility in Model 2 are more significant than Access 2Aand 2B in Model 1, and the goodness of fit of the model is higher in Model 2. The result
implies that considering the amount of time available in opportunities considering
space-time constraints is more useful.

6.CONCLUSIONS
It was obvious that aged people desire to do more in-home discretionary activities andout-of-home discretionary activities but some constraints prevent them from doing so
according to the present study. Especially because non-working people, who are moresatisfied with daily life, go-out more often and spend longer time in out-of-home activities,
participating in out-of-home discretionary activity affects level of satisfaction. By
developing the choice model, it was found that non-working aged people, who are older
and have higher accessibility, do out-of-home activities more frequently, and when facingat the same accessibility, go out more often in the daytime. In the choice models ofout-of-home and in-home activity participation, space-time accessibility is more significant
and introducing this variable improves the goodness of fit of the model.
Analyses using more detailed classification of both activity and opportunity, introducing
pedestrian environment to accessibility and considering trip chains of activities, would beneeded for further research. To better understand travel behavior and various constraints, itwould be useful to do in-depth interviews and to collect activity diaries for not one day but
multi-day or a week. Send-off trip is an important mode for aged people who can’t drive acar, so it would be valuable to collect activity diary data of all members in a household andto use a gaming simulation method to capture interaction among household members. On
the other hand, it would be necessary to develop better methods of collecting activity diarydata of aged people, because aged people bear the burden to fill in the questionnaire survey.
There are possibilities for more detailed and correct space-time travel behavior data being
collected automatically and the burden in the questionnaire survey being decreased, using
advanced information systems as GPS (Global Positioning System) and PHS (Personal
Handyphone System). GIS data concerning transportation and activity opportunity is notenough prepared. For example road network data exists, but not including time dimension,bus network data and activity opportunity data do not exist in most cities, and therefore it
was very hard work for us to make bus network data in this study. It would be useful iftransportation and activity opportunity GIS data are continuously prepared for this kind ofanalysis in a framework of space-time constraints.

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